Agent skill · product strategy · datadrivenconstruction
prefab-optimization
Optimize prefabrication and modular construction workflows. Plan module sequencing, factory scheduling, transportation logistics, and on-site assembly for maximum efficiency.
Why this skill is useful
Provides domain-specific algorithms for optimizing prefabrication workflows that the AI wouldn't reliably generate on its own.
What it needs
Requires python3 installed locally. About 7k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
Prefabrication Optimization Overview This skill implements optimization algorithms for prefabricated and modular construction. Maximize factory utilization, minimize transportation costs, and optimize on-site assembly sequences. Optimization Areas: Module design for transport Factory production scheduling Logistics and transportation On-site assembly sequencing Crane and equipment planning Quality control checkpoints Quick Start Comprehensive Prefab System Module Definition and Analysis Production Scheduling Assembly Sequence Optimization Transportation Optimization `python from scipy.optimize import linearsumassignment import numpy as np @dataclass class TransportVehicle: vehicleid: str capacitykg: float maxlengthm: float maxwidthm: float maxheightm: float costperkm: float availablefrom: datetime class TransportOptimizer: """Optimize module transportation logistics""" def init(self, factorylocation: Tuple[float, float], sitelocation: Tuple[float, float]): self.factory = factorylocation self.site = sitelocation self.distancekm = self.calculatedistance() self.vehicles: List[TransportVehicle] = [] self.routes: List[Dict] = [] def calculatedistance(self) -> float: """Calculate distance between factory and site""" # Simplified Haversine for demonstration lat1, lon1 = self.factory lat2, lon2 = self.site R = 6371 # Earth radius in km dlat = np.radians(lat2 - lat1) dlon = np.radians(lon2 - lon1) a = (np.sin(dlat/2)2 + np.cos(np.radians(lat1)) np.cos(np.radians(lat2)) np.sin(dlon/2)2) c = 2 np.arctan2(np.sqrt(a), np.sqrt(1-a)) return R c def addvehicle(self, vehicle: TransportVehicle): self.vehicles.append(vehicle) def optimizeloading(self, modules: List[ModuleDesign], requireddates: Dict[str, date]) -> Dict: """Optimize which modules go on which vehicle""" # Sort modules by required date sortedmodules = sorted( modules, key=lambda m: requireddates.get(m.moduleid, date.max) ) assignments = [] remainingmodules = list(sortedmodules) while remainingmodules: # Find best vehicle …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/prefab-optimization